Compare fitted bml models in a publication-style table

Builds a side-by-side regression table from one or more fitted bml models: one row per term, one column per model, with cells formatted as estimate (conf.low, conf.high). Goodness-of-fit rows (e.g. N and DIC) are appended at the bottom. The result is a data.frame of character cells, ready to pass to knitr::kable(), gt::gt(), etc.

For the underlying numeric values (e.g. to build custom tables or plots) use the broom methods tidy.bml and glance.bml directly; they also make modelsummary::modelsummary(list(m1, m2)) work on bml models.

Usage

bmlCompare(
  ...,
  component = "all",
  terms = NULL,
  labels = NULL,
  stats = c("N", "DIC"),
  digits = 3,
  conf.level = 0.95
)

Arguments

...: Fitted bml model objects, optionally named (e.g., bmlCompare(base = m1, weighted = m2)). The names become the column headers. Unnamed models are labeled with the expressions they were passed as (e.g., "m1").
component: Which parameters to include; passed to tidy.bml. One of "all" (default), "fixed", "random", or "weights".
terms: Optional character vector of term names selecting which rows to show and in what order (matched against the term labels from tidy.bml, e.g. "smoking_alter (mm.1)"). Terms not found are skipped with a warning. Default NULL: all terms in component, in their natural order.
labels: Optional character vector renaming the rows for display. Must be the same length as the selected terms (so supply terms when using labels). Default NULL: keep the original term labels.
stats: Goodness-of-fit rows to append, in order. Any of "N" (main-level units), "n.members" (member-level units), and "DIC". Default: c("N", "DIC"). Use character(0) for none.
digits: Number of decimal places for the estimate and interval bounds. Default: 3.
conf.level: Width of the equal-tailed credible interval. Default: 0.95. Other levels require the models to be fitted with monitor = TRUE (see tidy.bml).

Value

A data.frame whose first column Term holds the term labels (and the requested stats labels), followed by one character column per model containing estimate (conf.low, conf.high) cells.

Author

Benjamin Rosche benrosche@nyu.edu

Examples

if (FALSE) { # \dontrun{
data(coalgov)

m1 <- bml(
  Surv(dur_wkb, event_wkb) ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), w = w(~ 1/n), fn = fn("sum"), RE = TRUE),
  family = weibull(),
  data = coalgov
)
m2 <- bml(
  Surv(dur_wkb, event_wkb) ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), w = w(~ pseat), fn = fn("sum"), RE = TRUE),
  family = weibull(),
  data = coalgov
)

bmlCompare("Equal" = m1, "Seat share" = m2)

# Select and relabel specific rows
bmlCompare(
  "Equal" = m1, "Seat share" = m2,
  terms  = c("majority", "cohesion (mm.1)"),
  labels = c("Majority", "Cohesion")
) |> knitr::kable()
} # }